Churn Prediction: A Comparative Study Using KNN and Decision Trees

Mohammad A. Hassonah · Ali Rodan · Abdel-Karim Al-Tamimi · Jamal Alsakran

30 citations0/0 files storedHCT Information Technology Trends (ITT)

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Churn prediction represents one of the most important components of Customer Relationship Management (CRM). In the purpose of retaining customers and maintaining their satisfaction, researchers of many fields including business intelligence, marketing and information technology were motivated to investigate the best methods that deliver the best services for customers. Many machine learning algorithms had been implemented in the purpose of optimally predicting the possible churning customers and making the right decisions at the right moments. Researchers had conducted several studies on various types of algorithms and results were found very promising. In this paper, we are conducting a comparison study of the performance towards churn prediction between two of the most powerful machine learning algorithms which are Decision Tree and K-Nearest Neighbor algorithms. Results were quite interesting showing a quite large dissimilarity in many areas between the two algorithms.

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References

1990Zero defections: quality comes to services.1999Building Data Mining Applications for CRM 2005Applying data mining to telecom churn management 2008Customer churn prediction using improved balanced random forests 2005Computer assisted customer churn management: State-of-the-art and future trends 2013Telecommunication subscribers' churn prediction model using machine learning 2012Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies 2007Customer Churn Prediction Based on the Decision Tree in Personal Handyphone System Service

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